Allison T. Connolly

Neuromodulation for Brain Disorders: Challenges and Opportunities

The field of neuromodulation encompasses a wide spectrum of interventional technologies that modify pathological activity within the nervous system to achieve a therapeutic effect. Therapies including deep brain stimulation, intracranial cortical stimulation, transcranial direct current stimulation, and transcranial magnetic stimulation have all shown promising results across a range of neurological and neuropsychiatric disorders. While the mechanisms of therapeutic action are invariably different among these approaches, there are several fundamental neuroengineering challenges that are commonly applicable to improving neuromodulation efficacy. This paper reviews the state-of-the-art of neuromodulation for brain disorders and discusses the challenges and opportunities available for clinicians and researchers interested in advancing neuromodulation therapies.

Modulations in Oscillatory Frequency and Coupling in Globus Pallidus with Increasing Parkinsonian Severity

While beta oscillations often occur within the parkinsonian basal ganglia, how these oscillations emerge from a naive state and change with disease severity is not clear. To address this question, a progressive, nonhuman primate model of Parkinsons disease was developed using staged injections of MPTP. Within each parkinsonian state (naive, mild, moderate, and severe), spontaneous local field potentials were recorded throughout the sensorimotor globus pallidus. In the naive state, beta oscillations (11–32 Hz) occurred in half of the recordings, indicating spontaneous beta oscillations in globus pallidus are not pathognomonic. Mild and moderate states were characterized by a narrower distribution of beta frequencies that shifted toward the 8–15 Hz range. Additionally, coupling between the phase of beta and the amplitude of high-frequency oscillations (256–362 Hz) emerged in the mild state and increased with severity. These findings provide a novel mechanistic framework to understand how progressive loss of dopamine translates into abnormal information processing in the pallidum through alterations in oscillatory activity. The results suggest that rather than the emergence of oscillatory activity in one frequency spectrum or the other, parkinsonian motor signs may relate more to the development of altered coupling across multiple frequency spectrums.

Deep brain stimulation imposes complex informational lesions.

Deep brain stimulation (DBS) therapy has become an essential tool for treating a range of brain disorders. In the resting state, DBS is known to regularize spike activity in and downstream of the stimulated brain target, which in turn has been hypothesized to create informational lesions. Here, we specifically test this hypothesis using repetitive joint articulations in two non-human Primates while recording single-unit activity in the sensorimotor globus pallidus and motor thalamus before, during, and after DBS in the globus pallidus (GP) GP-DBS resulted in: (1) stimulus-entrained firing patterns in globus pallidus, (2) a monophasic stimulus-entrained firing pattern in motor thalamus, and (3) a complete or partial loss of responsiveness to joint position, velocity, or acceleration in globus pallidus (75%, 12/16 cells) and in the pallidal receiving area of motor thalamus (ventralis lateralis pars oralis, VLo) (38%, 21/55 cells). Despite loss of kinematic tuning, cells in the globus pallidus (63%, 10/16 cells) and VLo (84%, 46/55 cells) still responded to one or more aspects of joint movement during GP-DBS. Further, modulated kinematic tuning did not always necessitate modulation in firing patterns (2/12 cells in globus pallidus; 13/23 cells in VLo), and regularized firing patterns did not always correspond to altered responses to joint articulation (3/4 cells in globus pallidus, 11/33 cells in VLo). In this context, DBS therapy appears to function as an amalgam of network modulating and network lesioning therapies.

A Novel Lead Design for Modulation and Sensing of Deep Brain Structures

Goal: Develop and characterize the functionality of a novel thin-film probe technology with a higher density of electrode contacts than are currently available with commercial deep brain stimulation (DBS) lead technology. Such technology has potential to enhance the spatial precision of DBS and enable a more robust approach to sensing local field potential activity in the context of adaptive DBS strategies. Methods: Thin-film planar arrays were microfabricated and then assembled on a cylindrical carrier to achieve a lead with 3-D conformation. Using an integrated and removable stylet, the arrays were chronically implanted in the subthalamic nucleus and globus pallidus in two parkinsonian nonhuman primates. Results: This study provides the first in vivo data from chronically implanted DBS arrays for translational nonhuman primate studies. Stimulation through the arrays induced a decrease in parkinsonian rigidity, and directing current around the lead showed an orientation dependence for eliciting motor capsule side effects. The array recordings also showed that oscillatory activity in the basal ganglia is heterogeneous at a smaller scale than detected by the current DBS lead technology. Conclusion: These 3-D DBS arrays provide an enabling tool for future studies that seek to monitor and modulate deep brain activity through chronically implanted leads. Significance: DBS lead technology with a higher density of electrode contacts has potential to enable sculpting DBS current flow and sensing biomarkers of disease and therapy.

Computational modeling of an endovascular approach to deep brain stimulation

OBJECTIVE Deep brain stimulation (DBS) therapy currently relies on a transcranial neurosurgical technique to implant one or more electrode leads into the brain parenchyma. In this study, we used computational modeling to investigate the feasibility of using an endovascular approach to target DBS therapy. APPROACH Image-based anatomical reconstructions of the human brain and vasculature were used to identify 17 established and hypothesized anatomical targets of DBS, of which five were found adjacent to a vein or artery with intraluminal diameter ≥1 mm. Two of these targets, the fornix and subgenual cingulate white matter (SgCwm) tracts, were further investigated using a computational modeling framework that combined segmented volumes of the vascularized brain, finite element models of the tissue voltage during DBS, and multi-compartment axon models to predict the direct electrophysiological effects of endovascular DBS. MAIN RESULTS The models showed that: (1) a ring-electrode conforming to the vessel wall was more efficient at neural activation than a guidewire design, (2) increasing the length of a ring-electrode had minimal effect on neural activation thresholds, (3) large variability in neural activation occurred with suboptimal placement of a ring-electrode along the targeted vessel, and (4) activation thresholds for the fornix and SgCwm tracts were comparable for endovascular and stereotactic DBS, though endovascular DBS was able to produce significantly larger contralateral activation for a unilateral implantation. SIGNIFICANCE Together, these results suggest that endovascular DBS can serve as a complementary approach to stereotactic DBS in select cases.

Classification of pallidal oscillations with increasing parkinsonian severity

The firing patterns of neurons in the basal ganglia are known to become more oscillatory and synchronized from healthy to parkinsonian conditions. Similar changes have been observed with local field potentials (LFPs). In this study, we used an unbiased machine learning approach to investigate the utility of pallidal LFPs for discriminating the stages of a progressive parkinsonian model. A feature selection algorithm was used to identify subsets of LFP features that provided the most discriminatory information for severity of parkinsonian motor signs. Prediction errors <20% were achievable using 28 of the possible 206 features tested. For all subjects, a spectral feature within the beta band was chosen through the feature selection algorithm, but a combination of features, including alpha-band power and phase-amplitude coupling, was necessary to achieve minimal prediction errors. There was large variability between the discriminatory features for individual subjects, and testing of classifiers between subjects yielded prediction errors >50%. These results suggest that pallidal oscillations can be predictive biomarkers of parkinsonian severity, but the features are more complex than spectral power in individual frequency bands, such as the beta band. Additionally, the best feature set was subject specific, which highlights the pathophysiological heterogeneity of parkinsonism and the importance of subject specificity when designing closed-loop system controllers dependent on such features.

Physiological changes in the pallidum in a progressive model of Parkinson's disease: Are oscillations enough?

Neurophysiological changes in the basal ganglia thalamo-cortical circuit associated with the development of parkinsonian motor signs remain poorly understood. Theoretical models have ranged from those emphasizing changes in mean discharge rate to increased oscillatory activity within the beta range. The present study characterized neuronal activity within and across the internal and external segments of the globus pallidus as a function of motor severity using a staged, progressively severe 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine model of Parkinsonism in three rhesus monkeys. An increase in coherence between neuronal pairs across the external and internal globus pallidus was present in multiple frequency bands in the parkinsonian state; both the peak frequency of oscillatory coherence and the variability were reduced in the parkinsonian state. The incidence of 8-20Hz oscillatory activity in the internal globus pallidus increased with the progression of the disease when pooling the data across the three animals; however it did not correlate with motor severity when assessed individually and increased progressively in only one of three animals. No systematic relationship between mean discharge rates or the incidence or structure of bursting activity and motor severity was observed. These data suggest that exaggerated coupling across pallidal segments contribute to the development of the parkinsonian state by inducing an exaggerated level of synchrony and loss of focusing within the basal ganglia. These data further point to the lack of a defined relationship between rate changes, the mere presence of oscillatory activity in the beta range and bursting activity in the basal ganglia to the motor signs of Parkinsons disease.

Guiding deep brain stimulation contact selection using local field potentials sensed by a chronically implanted device in Parkinson's disease patients

We have found that a set of support vector machines operating upon local field potentials sensed from an implanted DBS lead can identify the contact chosen by the physician for the patients STN DBS therapy with 91% accuracy. The finding is based on a small data set and thus subject to change with further data collection and cross-validation. Nevertheless, the results suggest that an algorithm for selecting an effective contact for STN DBS based on the signals sensed from the DBS lead may be feasible.

Modulations in pallidal local field potentials in the systemic 1-methyl-4-phenyl-l, 2, 3, 6-tetrahydropyridine nonhuman primate model of Parkinson's disease during a voluntary reaching task

This case-study characterizes the changes in neuronal activity that occur within the globus pallidus (GP) in the behaving systemic 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine (MPTP) nonhuman primate model of Parkinsons disease (PD) while on and off dopaminergic therapy. Local field potentials (LFP) were recorded from a scaled 8-contact deep brain stimulation (DBS) lead during a center-out reaching task. Spectral LFP activity and reach behavior were correlated with parkinsonian motor signs and changes in behavior during dopaminergic treatment. Dopamine therapy i) increased reaction time and decreased reach time, ii) shortened the onset-times of LFP synchronization and desynchronization during reaction time, and iii) eliminated desynchronization of the high beta band. These findings suggest that dopamine-induced improvement in bradykinesia is related to a change in the pattern of synchronized oscillatory activity in GP.

A control-theoretic system identification framework and a real-time closed-loop clinical simulation testbed for electrical brain stimulation

OBJECTIVE Closed-loop electrical brain stimulation systems may enable a precisely-tailored treatment for neurological and neuropsychiatric disorders by controlling the stimulation based on neural activity feedback in real time. Developing model-based closed-loop systems requires a principled system identification framework to quantify the effect of input stimulation on output neural activity by learning an input-output (IO) dynamic model from data. Further, developing these systems needs a realistic clinical simulation testbed to design and validate the closed-loop controllers derived from the IO models before testing in human patients. APPROACH First, we design a control-theoretic system identification framework to build dynamic IO models for neural activity that are amenable to closed-loop control design. To enable tractable model-based control, we use a data-driven linear state-space IO model that characterizes the effect of input on neural activity in terms of a low-dimensional hidden neural state. To learn the model parameters, we design a novel input waveform-a pulse train modulated by stochastic binary noise (BN) parameters-that we show is optimal for collecting informative IO datasets in system identification and conforms to clinical safety requirements. Second, we further extend this waveform to a generalized BN (GBN)-modulated waveform to reduce the required system identification time. Third, to enable extensive testing of system identification and closed-loop control, we develop a real-time closed-loop clinical hardware-in-the-loop (HIL) simulation testbed using the [Formula: see text] microelectrode recording and stimulation device, which incorporates stochastic noises, unknown disturbances and stimulation artifacts. Using this testbed, we implement both the system identification and the closed-loop controller by taking control of mood in depression as an example. RESULTS Testbed simulation results show that the closed-loop controller designed from IO models identified with the BN-modulated waveform achieves tight control, and performs similar to a controller that knows the true IO model of neural activity. When system identification time is limited, performance is further improved using the GBN-modulated waveform. SIGNIFICANCE The system identification framework with the new BN-modulated waveform and the clinical HIL simulation testbed can help develop future model-based closed-loop electrical brain stimulation systems for treatment of neurological and neuropsychiatric disorders.